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• W2994788900 endingPage "111803" @default.
• W2994788900 startingPage "111803" @default.
• W2994788900 abstract "A full factorial experiment is performed for the conventional dry drilling of CFRP with spindle speed, feed rate and point angle as drilling parameters, response variables are thrust force and exit-delamination. Artificial neural network (ANN) is developed to express thrust force and delamination factor as a function of drilling parameters. Multi-objective optimization of drilling parameters is accomplished based on Non-dominated Sorting Genetic Algorithm (NSGA-II) with thrust force, delamination factor and material removal rate as optimization objectives, delamination factor also serves as a constraint. The Pareto front of drilling response variables determined by NSGA-II consists of a large number of non-dominated solutions. In order to facilitate the experimental verification of optimization results, fuzzy C-means clustering algorithm is used to narrow down the solutions on the front to several representative ones. Conformation tests are conducted and results show that the representative solutions can give satisfactory performance with achieving a trade-off among thrust force, exit-delamination and material removal rate." @default.
• W2994788900 created "2019-12-26" @default.
• W2994788900 creator A5070888010 @default.
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• W2994788900 date "2020-03-01" @default.
• W2994788900 modified "2023-09-30" @default.
• W2994788900 title "Multi-objective optimization of CFRP drilling parameters with a hybrid method integrating the ANN, NSGA-II and fuzzy C-means" @default.
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• W2994788900 doi "https://doi.org/10.1016/j.compstruct.2019.111803" @default.
• W2994788900 hasPublicationYear "2020" @default.
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